1. Field of Invention
The present invention relates to a dynamic image compression method, and more particularly to a dynamic image compression method for human face detection.
2. Related Art
In daily life nowadays, an image capture device is already widely used. The image capture device captures an image with a light sensor, and transforms the image into digital signals, which can be stored. Supported by digital image processing technologies, a variety of applications can be designed for the digital signals captured by the image capture device.
Human images are the most important among images captured by the image capture device. For example, currently many image capture devices have human face detection and human face tracking technologies, so as to automatically assist multiple focusing of a shooting area. In addition, the human face detection technology can also be utilized to judge whether humans exist within a certain area or not. For example, the human face detection technology is applicable for judging whether a user is watching a television screen in front of the television screen. When the human face detection technology judges that nobody is currently in front of the television screen, the television screen can be automatically turned off, so as to achieve an energy saving effect.
However, when the image capture device shoots a photographed object to acquire an image, if the photographed object is in an area having complicated light, in the image, brightness of a large part of the area might be too high and brightness of another large part of the area might be too low. The image that has many pixels concentrated at a bright portion and a dark portion at the same time is referred to as a high dynamic range image (HDRI). In the HDRI, too bright and too dark spots might lose original features of the image. That is to say, when the brightness of the human face is obviously too high or obviously too low, features of the human face might be lost, so that accuracy of human face detection is decreased.
In a conventional method, a brightness transformation function is directly utilized for correction. After the HDRI is corrected by using the brightness transformation function, the too bright spots can be adjusted darker and the too dark spots can be adjusted brighter. However, direct transformation blurs original features of the human face, for example, profiles of facial features. Therefore, accuracy of human face detection is lowered.